Practice Midterm Questions
1. Why are independent variables and dependent variables in Experimental Research instead called predictor variables and outcome variables in Predictive research models?
2. Please explain how ‘counterbalancing’ is a useful way to prevent practice and boredom effects in within-group designs?
3. Please answer the following questions:
What is meant by the ‘tails’ in distributions with skew and kurtosis?
Is a pointy distribution associated with heavy or light tails?
In which of the skew and kurtosis distributions might you need to include as many as 100 or 160 entities in samples?
4. List your own string of 11 ordered data points (any data points are fine) below and identify the 1) range of scores; 2) median; 3) quartiles - and name them; 4) Interquartile range.
5. As a sample size gets larger the standard error will get smaller. Why is this?
6. Why do we find it necessary for our studies to have a good amount of power?
If we set our power at .8 as generally recommended, what is our corresponding probability of failing to detect a genuine effect, and how do you know this.
7. We know that when dealing with linear models that statistical bias biases parameter estimates. But how does a bias associated with a parameter estimate then bias standard errors and confidence intervals? Please explain the process/elaborate, including discussion around sums of squares.
8. Why would a self-report depression measure, each question measured on a scale from 1-7, be considered an ordinal, discrete scale?
9. What assumption does the dataset clearly not meet in Plot 1 below? How do you know that the dataset did not meet this assumption?
What about the dataset in Plot 2 below? What two assumptions did the dataset in Plot 2 not meet? How do you know that the dataset did not meet these assumptions?
10. What issues do Levene’s test present when the sample size under consideration is large? What issues do Levene’s test present when the sample size under consideration is small?
11. Please refer to Table 1 below when reading and responding to this question. A researcher was interested in what factors influence people’s fear responses to horror films. She measured gender and how much a person is prone to believe in things that are not real (fantasy proneness) on a scale from 0 to 4 (0 = not at all fantasy prone, 4 = very fantasy prone). Fear responses were measured on a scale from 0 (not at all scared) to 15 (the most scared I have ever felt). How much variance (as a percentage) in fear is shared by gender and fantasy proneness in the population?
12. Recent research has shown that lecturers are among the most stressed workers. A researcher wanted to know exactly what it was about being a lecturer that created this stress and subsequent burnout. She recruited 75 lecturers and administered several questionnaires that measured:
Burnout
(high score = burnt out),
Perceived Control
(high score = low perceived control),
Coping Ability
(high score = low ability to cope with stress),
Stress from Teaching
(high score = teaching creates a lot of stress for the person),
Stress from Research
(high score = research creates a lot of stress for the person), and
Stress from Providing Pastoral Care
(high score = providing pastoral care creates a lot of stress for the person). The outcome of interest was burnout, and Cooper’s (1988) model of stress indicates that perceived control and coping style are important predictors of this variable. The remaining predictors were measured to see the unique contribution of different aspects of a lecturer’s work to their burnout.
Please refer to Table 2 below to in responding to the following question: How much variance in burnout does the final model explain for the sample?
How would you interpret the beta value for ‘stress from teaching’ in the final model (model 3)?
13. A kurtosis value of –2.89 implies a distribution that is what?
14. We learned that on average, samples from a normally distributed population will have skew/kurtosis of 0.
a. Please convert the values of skewness and kurtosis in Table below (Table 4) to a test of whether the values are significantly different from 0 usingz-scores. Please convert negative scores in to absolute values of those scores. Make sure to show your work by inserting the appropriate equations and corresponding numbers.
b. Please interpret your equations using complete sentences.
15. Please interpret Levene’s Test below.
16. A frequency distribution in which there are too few scores at the extremes of the distribution is said to be what?
17. If the scores on a test have a mean of 26 and a standard deviation of 4, what is the
z-score for a score of 18? (Please show your work!)
18. Explain why the log transformation is a good way to reduce positive skew.
19. A salesperson for a large car brand wants to determine whether there is a relationship between an individual's income and the price they pay for a car. As such, the individual's "income" is the predictor variable and the "price" they pay for a car is the outcome variable. The salesperson wants to use this information to determine which cars to offer potential customers in new areas where average income is known. He runs a Regression analysis. Given this information, please interpret R and R2
using Table 6 below. Please write in complete sentences, making sure to accurately indicate what both R and R2
mean.
Please refer to Table 7 below (Output from the same Regression Analysis run to produce Table 6) when reading and responding to the following questions:
· It is clear that the degrees of freedom for SSM
is 1, and the degrees of freedom for SSR
is 18. How did we get to these numbers? More specifically, what is the formula for the degrees of freedom for SSM
and SSR?
· How did we come to the outcome of 44182633.37 for the Regression Sum of Squares (SSM)?
· What does significance in this table mean?